Rule – Based Expert System

Definition:
Rule Based:
Using "if-this, do that" rules to perform actions. Rules-based products implies flexibility in the software, enabling tasks and data to be easily changed by replacing one or more rules.
Expert System:
An expert system is a computer program that simulates the judgement and behavior of a human or an organization that has expert knowledge and experience in a particular field.
Rule Based Expert System:
Rule-based Expert System: An expert system based on a set of rules that a human expert would follow in diagnosing a problem.

Benefits of Expert Systems

•Memungkinkan orang awam dapat mengerjakan pekerjaan para ahli
•Bisa melakukan proses berulang secara otomatis
•Menyimpan pengetahuan dan keahlian pakar
•Meningkatkan output dan produktifitas
•Melestarikan keahlian pakar
•Dapat beroperasi pada lingkungan berbahaya
•Dapat meningkatkan kemampuan sistem komputer
•Dapat bekerja dengan informasi yang tidak lengkap
•Sebagai media pelengkap dalam pelatihan
•Menghemat waktu pengambilan keputusan

The Down Side of Expert System
•Development of an ES is difficult
•ES is expensive
•Most ES still must be implemented & delivered on a big mainframe or minicomputer
•Not 100% reliable
•Kepakaran tidak selalu tersedia pada bidang-bidang tertentu

KEUNTUNGAN SISTEM BERBASIS ATURAN:
§ Ekspresi yang alamiah (natural)
§ Bagian Pengendali yang terpisah dengan Pengetahuan
§ Modularitas Pengetahuan
§ Mudah melakukan ekspansi sistem
§ Menggunakan pengetahuan yang relefan
§ Dapat menggunakan Pengetahuan Heuristik
§ Dapat menggunakan Pengetahuan yang Uncertainty
Dapat menggunakan variabel
KEKURANGAN/KERUGIAN SISTEM BERBASIS ATURAN:
§ Membutuhkan kondisi yang harus benar-benar tepat (exact matching) agar suatu rule dapat di-firing.
§ Tidak dapat melakukan override
§ Sistem bisa menjadi lambat pada set of rules yang besar

v Suatu Rule Terdiri dari 2 bagian, yaitu:
Ø Antacedent, yaitu bagian yang mengekspresikan situasi atau premis (Pernyataan berawalan IF)
Ø Konsekuen, yaitu bagian yang menyatakan suatu tindakan tertentu atau konklusi yang diterapkan jika situasi atau premis bernilai benar (Pernyataan berawalan THEN).
Misalnya:
IF lalulintas pagi ini macet
THEN saya naik sepeda motor saja
Other Example
A production system IDENTIFIER, which identifies animals.
R1 IF the animal has hair
THEN it is a mammal
R2 IF the animal gives milk
THEN it is a mammal
R3 IF the animal has feathers
THEN it is a bird
R4 IF the animal flies
the animal lays eggs
THEN it is a bird
R5 IF the animal is a mammal
the animal eats meat
THEN it is a carnivore
R6 IF the animal is a mammal
the animal has pointed teeth
the animal has claws
the animal's eyes point forward
THEN it is a carnivore
R7 IF the animal is a mammal
the animal has hooves
THEN it is an ungulate
R8 IF the animal is a mammal
the animal chews cud
THEN it is an ungulate AND
it is even-toed
R9 IF the animal is a carnivore
the animal has a tawny colour
the animal has dark spots
THEN it is a cheetah

R10 IF the animal is a carnivore
the animal has a tawny colour
the animal has black stripes
THEN it is a tiger
R11 IF the animal is an ungulate
the animal has long legs
the animal has a long neck
THEN it is a giraffe
R12 IF the animal is an ungulate
the animal has a white colour
the animal has black stripes
THEN it is a zebra
R13 IF the animal is a bird
the animal does not fly
the animal has long legs
the animal has a long neck
the animal is black and white
THEN it is an ostrich
R14 IF the animal is a bird
the animal does not fly
the animal swims
the animal is black and white
THEN it is a penguin
R15 IF the animal is a bird
the animal is a good flier
THEN it is an albatross

v Dua metode reasoning (penalaran) pada rules:
Ø Forward Chaining : pelacakan dimulai dari keadaan (informasi, fakta atau data) awal, dan kemudian mencocokan dengan tujuan yang diharapkan
Backward Chaining: Penalaran ini dimulai dari tujuan atau hipotesa, baru dicocokan dengan keadaan awal atau fakta yang ada.
Use Forward Chaining:
Given a certain set of facts in WM, use the rules to generate new facts until the desired goal is reached.
To forward chain the inference engine must:
1. Match the condition patterns of rules against facts in working memory.
2. If there is more than one rule that could be used (that could "fire"), select which one to apply (this is called conflict resolution)
3. Apply the rule, maybe causing new facts to be added to working memory
4. Halt when some useful (or goal) conclusion is added to WM (or until all possible conclusions have been drawn.)
Use Backward Chaining:
Work backwards from a hypothesised goal, attempting to prove it by linking the goal to the initial facts.
To backward chain from a goal in WM the inference engine must:
1. Select rules with conclusions matching the goal.
2. Replace the goal by the rule's premises. These become sub-goals.
3. Work backwards till all sub-goals are known to be true -
either they are facts (in WM)
or the user provides the information.